Input validation and sanitization in Agentic Ai - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 conceptual

intermediate

Why is input validation important in AI systems?

Imagine you have an AI model that predicts loan approvals. Why should you validate the input data before feeding it to the model?

A To increase the size of the dataset by adding random values.

B To make the model run faster by skipping some data points.

C To ensure the data is clean and within expected ranges, preventing wrong predictions.

D To avoid training the model on too much data.

💻 code output

intermediate

Output of input sanitization function

What is the output of this Python function that sanitizes input text by removing digits?

Agentic_ai

def sanitize(text):
    return ''.join(c for c in text if not c.isdigit())

result = sanitize('Agent007 is ready!')
print(result)

A Agent is ready!

B Agent007 is ready!

C 007 is ready!

D Agent is ready!

❓ model choice

advanced

Choosing a model robust to noisy input data

You have input data that may contain some errors or noise. Which model type is best to handle this situation?

A A simple linear regression model without regularization

B A deep neural network with dropout and batch normalization

C A decision tree with no pruning

D A nearest neighbor model using raw input features

❓ metrics

advanced

Evaluating input sanitization impact on model accuracy

You trained two models: one on raw input data and one on sanitized input data. The sanitized model has 5% higher accuracy. What does this suggest?

A Sanitizing input data improved data quality, helping the model learn better.

B Accuracy is not affected by input data quality.

C The model with raw data is always better regardless of accuracy.

D Sanitizing input data removed useful information, hurting the model.

🔧 debug

expert

Identifying input validation bug in AI pipeline

Consider this code snippet that validates numeric input features before prediction. What error will it raise?

Agentic_ai

def validate_features(features):
    for key, value in features.items():
        if value < 0:
            raise ValueError(f"Negative value for {key}")

input_data = {'age': 25, 'income': -5000, 'score': 70}
validate_features(input_data)

A TypeError: unsupported operand type(s) for <: 'str' and 'int'

B No error, function runs successfully

C KeyError: 'income'

D ValueError: Negative value for income